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Towards Time Series Sensor Data to Accurately Map Flood Hazard and Assess Damages under Climate Change Using Google Earth Engine Cloud Platform and GIS – Case of the Cities of Tetouan and Casablanca (Morocco)

El Yacoubi Yousra, Wahbi Miriam, Maatouk Mustapha, El Kharki Omar, Boulaassal Hakim, Alaoui Otmane Yazidi

Ecological Engineering & Environmental Technology

2023

Vol. 24, iss. 5

238--253

Climate change poses a major challenge in terms of urban planning management for the sake of a sustainable future. It is affecting the hydrological cycle around the world, leading to extreme weather conditions. Floods rank as the most frequent and widespread disaster in the world, they adversely affect inhabitants in terms of property damage and threat to human safety (and lives, in the worst cases). Uncontrolled urban sprawl also exacerbates floods by expanding impervious surfaces and affecting flow paths. Other factors that trigger flooding (apart from the rainfall intensity) are human involvement in the main waterways, thereby significantly impacting the hydraulic flow characteristics, structural engineering breakdowns, compounded by potential deforestation. For the purpose of monitoring the aftermath of floods experienced by the cities of Casablanca and Tetouan (Morocco) respectively in January and March 2021 and estimating their damages, optical and radar satellite images derived from the Google Earth Engine (GEE) cloud platform were used along with the Geographic Information System (GIS). In this study, a novel technique for extracting flooded areas from high-resolution Synthetic Aperture Radar (SAR) time series images has been developed. A comparison was carried out subsequently between the time-series approach and other traditional approaches including radiometric thresholding method, spectral indices namely Normalized Difference Water Index (NDWI) and Modified Normalized Difference Water Index (MNDWI) as well as Flood Water Index (FWI). Based on the above approach, the water levels were estimated and the damages were assessed and mapped, notably the number of people exposed to flood hazard and the amount of built-up areas and cropland affected. The results demonstrated that Casablanca city has witnessed a higher flood level than Tetouan city, putting a large number of people at risk and affecting a significant area of land use. The findings can also provide local authorities with a comprehensive view of flooding and enable them to make decisions on preparedness, mitigation, and adaptation to flood-related disasters.

Elastic full waveform inversion in VTI media using logging data constraints and gradient precondition

Xu Yipeng, Zhang Kai, Li Zhenchun, He Zilin, Gao Jinfeng, Zhao Xianyang

Acta Geophysica

2022

Vol. 70, no 4

1665--1676

Elastic full waveform inversion (EFWI) in VTI media using velocity model-based parameterization (PARM-VTIEFWI) can improve the sensitivity of anisotropic parameters, but there are still the problems of gradient’s interference and Nonunique. Research has shown that logging data can constrain the Non-unique of FWI. Therefore, in this paper, we use the logging data as a constraint to limit the Non-unique of the PARM-VTIEFWI. We derive the objective function and gradient equation for the PARM-VTIEFWI based on the constraint of logging information. In addition, to reduce the interference between the gradients of parameters in VTI media, we incorporate a pseudo-Hessian matrix to precondition the gradients and derive a gradient preconditioning formula based on the pseudo-Hessian matrix in this paper. In summary, we propose a new PARM-VTIEFWI method based on logging information constraint and gradient precondition, which reduces the interference between the parameter gradients and constrains the Non-unique of the inversion. The correctness and validity of the method were demonstrated by examples.

Full waveform inversion based on a local traveltime correction and zero-mean cross-correlation-based misft function

Dong Shiqi, Han Liguo, Hu Yong, Yin Yuchen

Acta Geophysica

2020

Vol. 68, no. 1

29--50

Full waveform inversion (FWI) sufers from the cycle skipping problem, because the observed data usually lack low-frequency components or due to errors in the wavelet estimation. In addition, the strong low-frequency non-zero-mean noise can have a large impact on FWI results. Thus, we propose a local waveform traveltime correction scheme to solve the situations when the observed data lack low-frequency components or when the estimation for the wavelet is incorrect. We use a sliding time window, which is used to decrease the traveltime diferences between the calculated and observed data to increase the cross-correlation between them. Besides, we propose a zero-mean normalized cross-correlation misft function to reduce the interference of the low-frequency non-zero-mean noise. Therefore, we propose new approaches to improve FWI results whether the observed data lack low-frequency components or the observed data are contaminated by the non-zero-mean lowfrequency noise. Numerical examples on Marmousi model show the feasibility of a FWI based on the zero-mean normalized cross-correlation misft function and a FWI based on the local traveltime correction method.